A total of 1,428 steer records of beef retail cuts collected by Hanwoo Improvement Center of Nonghyup, Korea were analyzed in this study. The steers were born between 2008 and 2012 and slaughtered at 24 months of age after the completion of corresponding (46th to 54th) Hanwoo progeny testing batches. These progeny test steers represented 145 young bulls in the National Hanwoo Improvement System of the Republic of Korea. Live body measures such as body weight (BWT) and 10 linear traits were taken at their yearling (12 mo) and slaughter (24 mo) ages.

Traits studied were yearling weight (YWT), BWT, chest girth (CG), body length (BL), chest depth (CD), chest width (CW), hip width (HW), rump width (RW), retail cut ratio (RCR), loin muscle ratio (LMR), cold carcass weight (CWT), and carcass backfat thickness (BFT). Trait details are presented in Table 1. The CG, BL, CD, HW, and RW traits were included in this study as an account for frame size which was associated with muscle mass growth. Cold carcass measurements were collected postmortem after an overnight chilling of the hot carcass. Beef carcass grading was performed at the same time. Ten primal cuts were obtained from all carcass samples according to the regulations once they were sent to a processing plant after completion of beef grading (see details in Choi et al [9]). The primal cut weights were recorded accordingly. The ratio of loin and sirloin together, also expressed as LMR, were calculated as the weight ratio of each cut to the total weight of retail cuts. However, the ratio of the retail cut indicates the ratio of sum weight of primal cuts to the respective carcass weight of the slaughtered animals in this study.

In Hanwoo progeny testing, each batch of animals is measured at an approximate 90 days interval when all animals attain a target test age on average. Therefore, as some animals differ from an exact test age during each test period, all BWT measures were pre-adjusted linearly from their previous live measurements. For YWT and BWT at 24 months of age, they were adjusted by a linear growth function of average daily gain through multiplying days and average daily gain from weight at 6 mo and 18 mo measures, respectively. A brief on the datasets and pedigree structure is shown in Table 2.

The live body measurements and carcass traits were analyzed simultaneously using two different animal models to estimate heritability and genetic correlation coefficients among the traits. Each model differed for a covariate effect, either carcass weight (model 1) or BFT (model 2), fitted to the carcass traits only, which were two cases of slaughter end points tried in the study of Wheeler et al [10] applied over various beef cattle breeds. These covariates were fitted in their first order linear forms. Carcass traits were also fitted with date of slaughter as a fixed effect which defined the effect of contemporary group fed, and the same slaughter and carcass processing environments. For live body measurements, however, respective batches of progeny tests were fitted as a contemporary group irrespective of models. A random animal breeding value effect was fitted with these mixed models as well. The variance and covariance components were estimated using restricted maximum likelihood (REML) based REMLF90 software package [11]. Two mixed model equations for carcass traits and live measurement traits tested for significance of the effects were as follows:

Where, y_ij is the carcass and live measurements of jth animal of ith contemporary group. The μ, cg, are means and fixed contemporary group effects (date of slaughter or batch of progeny test), respectively, The CWT and BFT are the covariate terms for jth animal and β is the partial regression coefficients related to each term. The BV_j is the random breeding value of jth animal. The e_ij is the random residual of the model.

The above two models can also be expressed using the same matrix notation for multivariate mixed models:

or

Where y is a vector of the observations for all n traits (y₁, y₂, …, y_n)^T; β is a vector of fixed effect solutions (β₁, β₂, …, β_n)^T for each trait (date of slaughter for carcass traits and batch of progeny tests for BWT and frame size measurement traits) and covariates (carcass wieght in Model 1 and BFT in Model 2); u is a vector of random additive genetic effect solutions (breeding values) for all n traits (u₁, u₂, …, u_n)^T; r is a vector of random residuals (r₁, r₂, …, r_n)^T; and, X and Z are incidence matrices that relate observations to fixed and random effects β and u, respectively.

And the covariance structure of random effects assumed was

Where, g_ii’s are the genetic variances of the ith trait and r_ii’s are the residual variances of the ith trait. And g_ij’s are the genetic covariances of ith and jth traits and r_ij’s are the residual covariances of ith and jth traits. A is the numerator relationship matrix among animals.

Sampling standard deviations of the (co)variances were estimated using AIREMLF90 package written by Shogo Tsuruta which uses the algorithm of Meyer and Houle [12].

The descriptive statistics of live body measurements and beef carcass cut ratios are shown in Table 1. It is observed that individual variation for BWT in Hanwoo cattle increased noticeably between 12 and 24 mo of age period. For frame size measurements (CG, BL, CD, HW, and RW), the changes in amount of variation were somewhat low to negligible with animal aging. However, the scalar increases of BL and CG deemed larger as compared to other frame size measures i.e., widths and depths. The coefficient of variation (CV) of live body measurements slightly decreased over time. The CV of BFT was relatively larger than those of CWT, RCR, and LMR. The lower phenotypic variation in RCR suggests that the selection progress for beef cut ratios could be slower over generations [13]. In this study, the average of YWT (350.00±39.79 kg), CWT (340.97±39.79 kg), and BFT (8.6±3.74 mm), and their CV estimates were greatly similar to those reported by Choi et al [9], though our obtained phenotypic ranges were slightly greater than their reports. Choi et al [14] also reported similar YWT from Hanwoo males. The CWT found in this study was in line with Moon et al [15] and Baik et al [16]. The averages of different loin cuts (tenderloin: 1.63%, striploin: 9.95%, and sirloin: 1.99%) reported by Choi et al [9] was somewhat lower than the present study. They also reported a total primal-cut average of 78.95%, which coincided greatly with RCR in this study. However, a study on Hanwoo steers by Lee et al [17] reported slightly lower meat yield (65.3%) than our study.

Table 3 illustrates the Pearson’s correlation coefficients between carcass and live body measurements. Most live body measurements (BL, CD, RW, CG, and HW), regardless of age, were negatively correlated with RCR and LMR at different magnitudes. Their correlations with LMR were relatively weak (−0.07 to −0.17) as compared to those with RCR (−0.14 to −0.42). These results deemed in agreement with Ort et al [8]. These live body measurements, on the contrary, revealed positive correlations with BFT and CWT where correlations were mostly low (0.02 to 0.27) and moderate to strong (0.42 to 0.81), respectively. The YWT and BWT also showed similar correlation trends with RCR, LMR, CWT, and BFT mostly (Table 3), as showed by body frame measures with others. Among the 24-month carcass measures, the correlation between RCR and LMR or between CWT and BFT was moderate and positive, by 0.56 or 0.41, respectively. However, the former two traits also showed negative correlations with latter two traits in most cases. Above results indicate that these body growth indicators in Hanwoo steers such as BWTs, carcass weight or carcass volume might have rather trivial relationships with compositional growth (BFT) that appears late in life.

The heritability (h²) estimates of all traits using model 1 and 2 are presented in Table 4. Estimated h² for body measurement traits were very similar from both models, although varied slightly at different ages. The h² estimates for BL, CW, CD, CG, RW, and HW were 0.23 to 0.25, 0.21 to 0.29, 0.28 to 0.31, 0.27 to 0.36, 0.26 to 0.27, and 0.20 to 0.22, respectively. Heritability estimates for YWT and BWT remained similar with different covariates in the models. Our h² estimate of YWT (0.27) lies within the previously reported range, 0.18 to 0.39, in Hanwoo and other breeds [14,6, 18–20]. For RCR, a moderate to high heritability range was obtained in this study, where h² estimates using model 1 and 2 were 0.56 and 0.47, respectively. The LMR tended to be somewhat more heritable with model 2 (0.42) than with model 1 (0.36). Earlier Choi et al [9] in Hanwoo males showed h² for particular loin cuts (tenderloin: 0.41; sirloin: 0.60; striploin: 0.64) instead of our estimate for overall LMR (0.36) which found to be seemingly in an overall agreement with the present estimate. Similar overall agreeable ranges for heritability were observed with Pabiou et al [21], which also estimated h² for sirloin, tenderloin, striploin and percentage of retail product. The h² reported for loins (0.07 to 0.48) by Cundiff et al [22] also deemed in close agreement with our figure. Nonetheless, the h² range of RCR in this study (0.47 to 0.56) was greatly supported by the total primal-cut heritability (0.52) in Choi et al [9], reviewed adjusted carcass lean percentage (0.47 to 0.55) in Koots et al [18], and predicted percentage retail cuts (0.49) in Benyshek [23]. Thus, our results indicate that selection for both RCR and LMR directly are likely to be effective in Hanwoo cattle because of their high heritability. Based on differences in h² for individual primal-cuts showed by Choi et al [9] and our estimates, our study also indicated that predicting genetic merit for particular meat-cuts rather than gross meat-cut proportions i.e. LMR or RCR could be a good alternative for effective selection improvement. However, the differences between models estimates could be caused by some genetic variations of carcass compositions remained hidden by the variations of body fat reserves in different forms and localities [24, 13, 25–27].

The genetic correlation (r_G) estimates of all live body measurements with RCR were generally low negative to low positive across the models such as −0.32 to 0.13 (model 1) and −0.21 to 0.19 (model 2). Genetically, RCR showed almost none to very low positive or low negative correlations with BL, CD, RW, and HW regardless of models or ages of animals. The LMR also expressed similar genetic relationships with these body measurement traits except for their magnitude. Either CWT or BFT fitted as covariates, CG deemed genetically more negatively correlated with RCR at older age. The correlations between LMR and some linear traits (BL, CW, CD, and CG), with CWT or BFT fitted models were somewhat similar but with opposite trends.

Genetically, RCR deemed almost independent of BWT and YWT, irrespective of covariates fitted. In this regards, Choi et al [9] showed similar none or low correlation (0.17±0.16) between YWT and total primal-cut yield. The LMR, on the contrary, was either negatively (r_G: −0.31; model 1) or positively (r_G: 0.11; model 2) related to BWT based on slaughter endpoints. Both models also revealed similar trends between LMR and YWT showing r_G of −0.18 and 0.29 with CWT and BFT as slaughter endpoints, respectively. As Choi et al [9] studied various loin-cuts, the only correlation they found to be different from zero existed between striploin and YWT (0.35±0.14). This deemed to agree with our study when BFT was fitted as covariate. Perhaps, the adjustment for BFT as covariate might have partitioned some variation in the trait that were unrelated to loin muscles but fat contents, and thus predicted a less biased estimate for LMR. Also, our r_G estimate greatly coincided with the correlation of YWT and most heritable loin-cut (striploin) in Choi et al [9]. This resemblance between correlations deemed more reasonable when the greater contribution of striploin to the total loin-cut region (73%; [3]) was considered. The genetic relationship between RCR and LMR fitting CWT as slaughter endpoint was 0.64 (Table 4), whereas fitting with BFT estimated an r_G of 0.50. Again, our correlation estimate (adjusted for BFT) coincided with the r_G between total primal-cuts and striploin (r_G: 0.53) as reported by Choi et al [9,3]. Thus, it may suggest that the genetic merit of overall loin cuts ratio or striploin in particular could be better estimated with BFT as slaughter endpoint than with CWT.

The phenotypic correlations of RCR with most body measurements, body growth and carcass measurements were equal or somewhat strongly negative relative to the r_G estimates of the same model. Between models, the CWT as covariate revealed relatively higher negative estimates than fitting BFT for above traits. The LMR also revealed very similar lower negative phenotypic correlations than their respective model r_G estimates, which mostly stand close to zero, with body measurements as well as body growth and carcass traits measures.

From the genetic standpoint, the observed estimates indicate some possible selection scenario. If selection is targeted on populations where animals are to be slaughtered to obtain a certain carcass weight, the smaller body measurements and growth trait measures in all traits at any age for animals would contribute to a better LMR, whereas only a few (CW, CD, CG) might contribute to RCR to a certain proportion. If certain degree of BFT is aimed at slaughter, a selection of seedstock animals for greater weights or frame size measures might not be feasible enough as compared to the direct selection on genetic merit of LMR itself through progeny testing. The moderate to high heritability estimates as well as their correlations between them or with others greatly indicated that a direct selection on carcass traits, LMR in particular would provide more proportional gains to the other trait, instead of any indirect selection schemes. Nevertheless, the CW might be the only candidate trait for selection of animals at an earlier age with respect to any of the slaughter endpoints.

Our results, as summarized, suggest that the genetic basis for RCR or LMR might rather be less straight forward with regard to other body growth measurement traits. Thus, any direct selection strategy on these traits such as selection of superior animals could be more useful than any indirect approach. Based on previous reports that each individual carcass retail cuts might inherit differently over generations, a prudent selection strategy on each desired cuts other than overall retail-cuts ratio or LMR could ensure more desirable and faster genetic progress. For live body measurements, there exist some complex and functional trade-off relationships among traits as each animal grow older, which may cause selection (at an early age) gains less predictable. Therefore, to conclude, the BWTs or linear body measurements at an earlier age may not be the most desirable selection traits for exploitation of correlated responses to improve loin muscle or lean meat yield. We believe that this study also provides adequate emphasis for further large scale analyses in order to understand the genetic merit and the connectedness of the less studied primal cut or retail-cut traits in Hanwoo cattle.